Hybrid binary grey wolf naked mole-rat algorithm for fragment-type UWB antenna optimization using time-varying transfer functions

Abstract

This paper introduces a novel hybrid binary variant of the Grey Wolf Optimizer (GWO) and the Naked Mole-Rat Algorithm (NMRA), called HbGNMR. The primary objective of this hybridization is to overcome the poor exploration and local optima stagnation problems of the basic NMRA as well as the poor exploitation of GWO. HbGNMR incorporates the global search strategy of GWO into the worker phase to enhance exploration, and a mating factor based on simulated annealing-inertia weight is added to the breeder phase to improve exploitation. Moreover, fourteen new time-varying binary transfer functions of two families: seven S-shaped and seven V-shaped functions have been analysed. Among these transfer functions, TVV5 was identified as the most effective in converting solutions from continuous search space to binary search space and maintaining a balance between exploration and exploitation phases. The hybrid performance of HbGNMR is evaluated on twenty-three CEC’05, thirty CEC’17, and eighty CEC’21 benchmark functions. The statistical results demonstrate that HbGNMR outperforms nine well-known state-of-the-art algorithms according to Friedman-test and Wilcoxon rank-sum test. To test the effectiveness of the proposed algorithm for real-world applications, HbGNMR is employed to optimize a large dimensional planar-monopole ultrawideband (UWB) fragment-type antenna structure of an 18 × 18 binary matrix cells. Experimental testing of the prototype is conducted on a vector-network-analyser-based experimental-testbed, which shows good agreement with the simulated results. Furthermore, the optimized structure achieves large bandwidth (3.1-12.6 GHz), excellent gain, and high directivity for the 10 × 15 mm2 compact size UWB antenna geometry. Therefore, the optimized antenna can be utilized for modern wireless applications.